Give a defined business task an agent that can understand the request and act within its remit.
Anteomnio builds AI agents for customer service, sales enquiries and internal operations. We connect the information and tools they need, establish where human review belongs, and evaluate the work before widening their responsibility.
An AI agent uses a model to interpret a request and select actions through permitted tools. It might retrieve an order, find a policy, ask for missing information and prepare a support case.
The useful distinction is the work it can complete. A convincing conversation has little value if your team still has to reconstruct the request and enter every detail afterward.
We begin with a specific task, an observable outcome and a defined limit of authority. That makes the agent easier to test, easier to operate and easier to judge against the process it is intended to improve.
| Approach | A useful fit | What to establish |
|---|---|---|
| Chatbot or knowledge assistant | Helping people find information through a conversation. | Which sources support the answer and when to escalate. |
| Workflow automation | Moving known inputs through repeatable, predefined steps. | Triggers, rules, exceptions and recovery from failed actions. |
| AI agent | Interpreting varied requests and selecting from permitted actions. | Task boundaries, tool permissions, evaluation and human review. |
These approaches can be combined. A conversational interface can collect a request, an agent can interpret it, and a conventional workflow can control the transaction.
Explore business process automation ↗Repeated requests rarely arrive in the same words. An agent can help turn that variation into a structured next step, provided the information, permissions and review process are in place.
Retrieve approved guidance and authorised account details, ask for missing information and prepare a useful response. Escalate exceptions with the conversation and evidence attached.
We define which replies may be sent automatically and which need review. Refunds, replacements and account changes each require an explicit permission decision.
An AI sales agent can collect relevant requirements, ask agreed qualification questions and prepare a CRM handoff. Your team receives a structured account of the enquiry with unanswered questions clearly identified.
We define contact rules and permitted claims. The agent should not invent availability, promise a price or imply that a proposal has been accepted.
Interpret an incoming message, retrieve the relevant project and prepare a task with supporting context. Requests that affect scope, cost or commitments can go to the responsible person before any change is made.
For tasks centred on reading documents or finding internal information, we can scope a focused document-processing or knowledge-assistant solution.
In this illustrative support workflow, the agent gathers the facts and prepares a case. A person retains authority to approve the replacement. That boundary is enforced in the application’s tools and permissions.
Confirm the requester can access the order before retrieving its details.
Read the order status and current replacement guidance. Keep source references with the case.
Ask for the required photograph if it has not been supplied. Leave the case awaiting evidence.
Summarise the request and attach the available evidence for the authorised reviewer.
We enforce permissions and validation around the tools the agent uses. A request in an email, document or conversation must not grant the agent additional authority.
The question is how the agent behaves across the cases your business actually sees. We agree representative examples, expected outcomes and unacceptable actions before deciding what is ready to release.
Check the resulting record, response or handoff against the expected outcome. Record when human correction is still needed.
Use missing details, outdated guidance and conflicting records. Check that the agent asks, stops or escalates rather than filling gaps with assumptions.
Test requests for another account’s data, unauthorised actions and instructions embedded in retrieved content.
Check unavailable systems, timeouts and uncertain results. Define how the task is handed over and prevent unsafe repetition of actions.
Acceptance thresholds are agreed for the specific task. AI outputs can still be wrong; ongoing review and evaluation remain part of operating the service.
An agent needs dependable access to the records and actions its job requires. We assess APIs, data quality and account permissions before promising an integration.
We also establish which information may reach a model provider, how credentials are managed, what is recorded and how long records are retained. These choices depend on your data and operating requirements.
Review real requests, current effort and the outcome you want. Confirm whether an agent is justified or a simpler workflow would be sufficient.
Agree sources, tools, authority, review points and acceptance checks. The proposal sets out deliverables and a fixed price for the agreed work.
Weekly updates and a preview make behaviour available for review. Test representative and difficult cases, then adjust the implementation before a controlled pilot.
Assign responsibility for escalations, access, quality checks and future changes. Hand over the agreed code, access and documentation. Launch fixes cover 30 days; continued evaluation and maintenance are scoped separately.
Expand the agent’s remit when the results support it. Adding another tool or task changes what needs to be tested and supervised.
An AI agent is software that uses an AI model to interpret a request and select actions using permitted tools. In a business application, those tools might retrieve an order, prepare a response or create a task. Its scope should define which information it can access, which actions it can take and when a person must review the result.
A chatbot describes a conversational interface; it may answer questions without taking action in another system. A workflow follows predefined steps. An AI agent can select from permitted actions based on the request and available context. These approaches can work together: a chat interface receives a request, an agent interprets it and a defined workflow controls the resulting transaction.
A customer-service agent can retrieve approved help content and authorised account information, prepare an answer, collect missing details and create a support task. Refunds, account changes and other consequential actions can require human approval. The implementation should define authentication, escalation and how unsupported answers are handled.
Yes. Within an agreed scope, an agent can ask relevant qualification questions, organise enquiry details and prepare a CRM record or handoff for your sales team. We define the information it may use, contact rules and the claims it may make. Pricing commitments and unusual requests can remain with a person.
Development cost depends on the task, system integrations, data preparation, permissions and evaluation required. Ongoing costs may include model usage, hosting, third-party tools and maintenance. Anteomnio scopes a bounded first release before providing a written price. We also consider expected task volume and review effort when assessing whether the investment makes sense.
Yes, where suitable APIs and access controls are available. We define the records the agent can retrieve, the actions it may request and the validation required before writing changes. Integration credentials and permissions are restricted to the agreed task. Unsupported or incomplete integrations are identified during discovery.
Yes. We can connect an agent to selected, maintained sources and enforce the access rules required by the task. Retrieval helps supply relevant information, but does not guarantee a correct answer. We evaluate whether responses are supported by the retrieved material and define what happens when sources are missing, outdated or contradictory.
We combine bounded tasks, approved sources, restricted tools, input validation and human review at agreed decision points. Evaluation includes incorrect or missing data, misleading requests and attempts to make the agent act outside its scope. These controls reduce risk; they do not make AI infallible. An operating process is needed to review failures and update the system.
They can be, when a repeatable task has enough volume and a clear outcome. A narrow agent for enquiries or service requests is easier to evaluate than a general assistant responsible for many parts of the business. If a form, existing product or rule-based automation solves the problem adequately, that may be the better starting point.
We agree who reviews escalations, checks quality, manages access and approves changes. Handover includes the agreed implementation, access and operating documentation. Our standard handover includes 30 days of launch fixes; ongoing monitoring, evaluation, model changes and new capabilities are scoped separately.
Bring a recurring request, the systems involved and the decisions your team needs to retain. We will help define a task worth testing and a practical way to evaluate it.
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